






"""simple docstring"""
import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowerCamelCase_							      = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''store_true''', help='''Enable DPMSolver or not''')
parser.add_argument('''--steps''', default=None, type=int, help='''Num inference steps''')
lowerCamelCase_							      = parser.parse_args()


lowerCamelCase_							      = '''cpu'''
lowerCamelCase_							      = '''a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'''

lowerCamelCase_							      = '''path-to-your-trained-model'''
lowerCamelCase_							      = StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
			lowerCamelCase_							      = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
lowerCamelCase_							      = pipe.to(device)

# to channels last
lowerCamelCase_							      = pipe.unet.to(memory_format=torch.channels_last)
lowerCamelCase_							      = pipe.vae.to(memory_format=torch.channels_last)
lowerCamelCase_							      = pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
			lowerCamelCase_							      = pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
lowerCamelCase_							      = torch.randn(2, 4, 64, 64)
lowerCamelCase_							      = torch.rand(1) * 999
lowerCamelCase_							      = torch.randn(2, 77, 768)
lowerCamelCase_							      = (sample, timestep, encoder_hidden_status)
try:
			lowerCamelCase_							      = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
			lowerCamelCase_							      = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowerCamelCase_							      = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowerCamelCase_							      = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
			lowerCamelCase_							      = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowerCamelCase_							      = 666
lowerCamelCase_							      = torch.Generator(device).manual_seed(seed)
lowerCamelCase_							      = {'''generator''': generator}
if args.steps is not None:
			lowerCamelCase_							      = args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
			lowerCamelCase_							      = pipe(prompt, **generate_kwargs).images[0]

# save image
image.save('''generated.png''')





from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase    :   Union[str, Any]						=			logging.get_logger(__name__)

lowercase    :   Union[str, Any]						=			{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'efficientformer'







	def __init__(   self						:Union[str, Any]	,  a						:List[int] = [3, 2, 6, 4]	,  a						:List[int] = [4_8, 9_6, 2_2_4, 4_4_8]	,  a						:List[bool] = [True, True, True, True]	,  a						:int = 4_4_8	,  a						:int = 3_2	,  a						:int = 4	,  a						:int = 7	,  a						:int = 5	,  a						:int = 8	,  a						:int = 4	,  a						:float = 0.0	,  a						:int = 1_6	,  a						:int = 3	,  a						:int = 3	,  a						:int = 3	,  a						:int = 2	,  a						:int = 1	,  a						:float = 0.0	,  a						:int = 1	,  a						:bool = True	,  a						:bool = True	,  a						:float = 1E-5	,  a						:str = "gelu"	,  a						:float = 0.02	,  a						:float = 1E-1_2	,  a						:int = 2_2_4	,  a						:float = 1E-0_5	,  **a						:str	,  )						->					None:
								super().__init__(**a		)

								__UpperCamelCase      :  Optional[Any]       =   hidden_act
								__UpperCamelCase      :  Any       =   hidden_dropout_prob
								__UpperCamelCase      :  str       =   hidden_sizes
								__UpperCamelCase      :  Dict       =   num_hidden_layers
								__UpperCamelCase      :  Optional[Any]       =   num_attention_heads
								__UpperCamelCase      :  str       =   initializer_range
								__UpperCamelCase      :  List[str]       =   layer_norm_eps
								__UpperCamelCase      :  str       =   patch_size
								__UpperCamelCase      :  str       =   num_channels
								__UpperCamelCase      :  Any       =   depths
								__UpperCamelCase      :  Tuple       =   mlp_expansion_ratio
								__UpperCamelCase      :  List[str]       =   downsamples
								__UpperCamelCase      :  Optional[int]       =   dim
								__UpperCamelCase      :  Dict       =   key_dim
								__UpperCamelCase      :  Optional[Any]       =   attention_ratio
								__UpperCamelCase      :  Dict       =   resolution
								__UpperCamelCase      :  Union[str, Any]       =   pool_size
								__UpperCamelCase      :  Tuple       =   downsample_patch_size
								__UpperCamelCase      :  Optional[int]       =   downsample_stride
								__UpperCamelCase      :  Optional[int]       =   downsample_pad
								__UpperCamelCase      :  Union[str, Any]       =   drop_path_rate
								__UpperCamelCase      :  Union[str, Any]       =   num_metaad_blocks
								__UpperCamelCase      :  List[str]       =   distillation
								__UpperCamelCase      :  str       =   use_layer_scale
								__UpperCamelCase      :  Tuple       =   layer_scale_init_value
								__UpperCamelCase      :  str       =   image_size
								__UpperCamelCase      :  int       =   batch_norm_eps